The disadvantage of both GCN and GAT architectures is that they are static and do not take into account the dynamic changes in the network that happen over time. To take into account the time dimension, we use the GNN models mentioned above together with Recurrent Neural Networks, namely the Long Short-Term Memory (LSTM) model [1]} and Gated Recurrent Units (GRUs) model [2]}. The LSTM model is capable of learning long-term dependencies by storing long-term memory in a cell state while capturing the most recent information in its hidden states. The GRU model is a less complex version of LSTMs as it does not have a cell state and stores long-term memory directly in its hidden states. In both the LSTM and GRU models, the output embeddings of a GNN model are used as an input.
